In Keras, initializers are a set of functions that are used to initialize the weights and biases of neural network layers. The "initializers" module in Keras provides several options for initializing weights, such as random normal, random uniform, and Glorot uniform.
Here's an example of how to use the initializers module in Keras:
pythonfrom keras.models import Sequential
from keras.layers import Dense
from keras import initializers
model = Sequential()
model.add(Dense(64, input_dim=100, kernel_initializer='random_normal'))
model.add(Dense(32, kernel_initializer=initializers.RandomUniform(minval=-0.05, maxval=0.05)))
In the example above, we are creating a sequential model with two dense layers. The first layer has 64 units and is initialized using the "random_normal" initializer from the initializers module. The second layer has 32 units and is initialized using the "RandomUniform" initializer with a minimum value of -0.05 and a maximum value of 0.05.
By default, Keras uses the "glorot_uniform" initializer, which is designed to sample weights uniformly from a range that is dependent on the number of input and output units. However, you can choose to use any of the other initializers available in the "initializers" module to suit your specific needs.
Keywords: Keras initializers, weight initialization, neural network initialization, random normal initializer, random uniform initializer, Glorot uniform initializer, how to use Keras initializers.
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